Estimation of Sideslip Angle and Tire Cornering Stiffness Using Fuzzy Adaptive Robust Cubature Kalman Filter

Wang, Yan; Geng, Keke; Xu, Liwei; Ren, Yaping; Dong, Husheng; Yin, Guodong

doi:10.1109/tsmc.2020.3020562

Cited by 37 publications

(7 citation statements)

References 32 publications

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“…The normal operation of the active safety system is based on the accurate acquisition of vehicle dynamics, such as vehicle speed and sideslip angle. For vehicle stability control systems, vehicle speed and sideslip angle are the key control targets that determine the vehicle trajectory [ 3 ]. In addition, tires are the only component contacting the vehicle and the ground, and the tire forces generated by them can dominate the vehicle’s motion.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Feng

et al. 2023

Entropy

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Accurate acquisition of vehicle dynamics state information is essential for vehicle active safety control systems. However, these states cannot be easily measured, and the measurement is expensive. Conventional Kalman filters perform well for vehicle state estimation in Gaussian environments but exhibit low accuracy and robustness under practical non-Gaussian noise. Vehicle model parameter ingestion, inaccurate tire force calculation, and non-Gaussian noise from on-board sensors cause great challenges to the estimation of vehicle driving states. Therefore, this paper presents a robust hierarchical estimation scheme for vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF) using easily measurable on-board sensor information. First, the vehicle mass is dynamically updated based on the recursive least squares (FRLS) method with a forgetting factor. Then, an adaptive sliding mode observer (ASMO) is designed to estimate the longitudinal and lateral tire forces. Ultimately, the vehicle states are estimated based on the MCSCKF under non-Gaussian noise. Two typical operating situations are carried out to verify the validity of the proposed estimation scheme. The results prove that the proposed estimation scheme can estimate the vehicle’s driving state accurately compared to other common methods. And the MCSCKF algorithm has better accuracy and robustness than the traditional Kalman filters for vehicle state estimation in non-Gaussian situations.

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Section: Introductionmentioning

confidence: 99%

“…The vehicle mass variation has an important effect on the accuracy of the vehicle dynamics model [ 3 , 23 ]. Inaccurate vehicle mass will increase the estimation error for vehicular tire forces and states, so the vehicle mass variation factor should be considered.…”

Section: Introductionmentioning

confidence: 99%

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Feng

et al. 2023

Entropy

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“…Katriniok et al [1] proposed an extended Kalman lterbased estimator to estimate the vehicle's yaw rate and the longitudinal and lateral velocity. Wang et al [2] estimated the tire cornering sti ness and the sideslip angle using a novel fuzzy adaptive robust cubature Kalman lter (FARCKF). Chen et al [3] estimated the longitudinal force, yaw rate, and the lateral vehicle speed for four-wheel independent driven electric vehicle using a fusion method.…”

Section: Introductionmentioning

confidence: 99%

Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter

Liu

Cui

2022

Mathematical Problems in Engineering

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Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area.

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“…In order to improve handling stability performance and active safety of a ground vehicle, Jin et al 2 presented a survey in different aspects of estimation strategies and methodologies. Wang et al 3 proposed a novel fuzzy adaptive robust cubature Kalman filter to estimate sideslip angle and tire cornering stiffness with only in-vehicle sensors. Boada et al 4 provided a state-parameter observer using measurements from sensors by establishing a vertical complete model based on the dual Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Estimation algorithm for vehicle state estimation using ant lion optimization algorithm

Liu

Cui

2022

Advances in Mechanical Engineering

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In order to solve the problem of vehicle state estimation, an unscented Kalman filter state parameter estimator based on the Ant lion algorithm is proposed. Aiming at the uncertainty of the noise covariance matrix in the unscented Kalman filter (UKF) process, the Ant lion optimization algorithm (ALO) is used to optimize it. Based on the purpose, a 3-DOF nonlinear vehicle estimation model with Magic formula tire model was established firstly. Then the slalom road operating condition was simulated. The simulation results show that the estimated values of the key state variables are in better agreement with the virtual test values indicating the proposed algorithm having a good estimation performance. And also, compared with the estimation results of the UKF algorithm, the maximum error and the root mean square error of the estimation algorithm proposed in this paper are both smaller than the estimated value of the UKF algorithm. The results of a real-vehicle experiment demonstrate that the proposed method can be used effectively and accurately for solving the vehicle-state estimation problem. The study can provide precise status information for vehicle stability control under extreme conditions.

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Estimation of Sideslip Angle and Tire Cornering Stiffness Using Fuzzy Adaptive Robust Cubature Kalman Filter

Cited by 37 publications

References 32 publications

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter

Estimation algorithm for vehicle state estimation using ant lion optimization algorithm

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